In [1]:
%matplotlib inline
from IPython.display import HTML,Image,SVG,YouTubeVideo
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from skimage import data
import numpy as np
from skimage.morphology import disk
import skimage.filters.rank as skr
from skimage.measure import label
from skimage.morphology import watershed
from skimage.io import imread
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
In [3]:
# segment the coins
im = data.coins()
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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# detect the eyes / nose
im = data.chelsea()
plt.imshow(im);
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# counting the galaxies
im = data.hubble_deep_field()
plt.imshow(im);
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im = data.page()
bg = skr.median(im, disk(10))
res = (1.*im/bg) < .8
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(bg,cmap=plt.cm.gray);
plt.colorbar()
plt.figure()
plt.imshow(res.astype(np.uint8),cmap=plt.cm.gray);
plt.colorbar();
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# segment the cells
im = imread('../data/dh_phase.png')
th = im>150
th1 = im>100
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(1.*th+th1,cmap=plt.cm.gray)
plt.colorbar();
In [8]:
from skimage.feature import canny
ca = canny(im)
plt.figure(figsize=[10,10])
plt.imshow(ca,cmap=plt.cm.gray);
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from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
lab,n_lab = label(th,return_num=True)
bg = th1==0
lab[bg] = n_lab+1
#med = skr.median(im,disk(5))
#gr = skr.gradient(med,disk(3))
ws = watershed(255-im,lab)
plt.imshow(mark_boundaries(im,ws))
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead. def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
Out[9]:
<matplotlib.image.AxesImage at 0x7fcd21290e90>
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im = imread('../data/exp0001.jpg')
plt.figure(figsize=[20,20])
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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# count red and yellow flowers
im = imread('../data/flowers.jpg')
plt.imshow(im)
plt.colorbar();
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# find the fiber orientation
im = imread('../data/image4.png')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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from IPython.display import YouTubeVideo
YouTubeVideo('PUcz11MLxUk', start=0, autoplay=1, theme="light", color="blue",)
Out[13]:
In [14]:
# detect stroma
im = imread('../data/Rp042826d.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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# segment the flowers
im = imread('../data/KaneFlowers.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
gr = skr.gradient(im,disk(3))
local_min = im <= skr.minimum(im,disk(5))
lab = label(local_min)
#med = skr.median(im,disk(5))
ws = watershed(gr,lab)
plt.figure(figsize=[10,10])
plt.imshow(mark_boundaries(im,ws))
#plt.imshow(local_min)
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead. def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
Out[16]:
<matplotlib.image.AxesImage at 0x7fcd2056ee10>
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rgb = imread('../data/4colors.JPG')
plt.figure(figsize=[20,20])
plt.imshow(rgb)
plt.colorbar();
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r = skr.median(rgb[:,:,0],disk(1))
plt.imshow(r,cmap=plt.cm.gray)
Out[18]:
<matplotlib.image.AxesImage at 0x7fcd203ff590>
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s = rgb.sum(axis=2)
th = s > 100
#post-processing
pth = skr.minimum(th.astype(np.uint8),disk(1))
plt.figure(figsize=[20,20])
plt.imshow(pth,cmap=plt.cm.gray)
plt.colorbar()
Out[19]:
<matplotlib.colorbar.Colorbar at 0x7fcd2060dd10>
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lab = label(pth)
lut = np.arange(0,np.max(lab)+1)
plt.imshow(lab)
plt.colorbar()
mask = lab == 20
plt.imshow(mask)
Out[20]:
<matplotlib.image.AxesImage at 0x7fcd2121c6d0>
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from random import shuffle
shuffle(lut)
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shuffle(lut)
plt.imshow(lut[lab])
plt.colorbar()
Out[22]:
<matplotlib.colorbar.Colorbar at 0x7fcd4ea375d0>
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In [23]:
# segment the cell
im = imread('../data/exp0001crop.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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m = skr.median(im,disk(5))
plt.imshow(m,cmap=plt.cm.gray)
plt.colorbar()
Out[24]:
<matplotlib.colorbar.Colorbar at 0x7fcd2129e050>
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th1 = m < 90
th2 = np.bitwise_and(110 > m,m < 130)
plt.imshow(th2)
Out[25]:
<matplotlib.image.AxesImage at 0x7fcd20f4f290>
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markers = label(th2)
plt.imshow(markers)
plt.colorbar()
Out[26]:
<matplotlib.colorbar.Colorbar at 0x7fcd211f0e50>
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markers[markers==3] = 2
ws = watershed(im,markers)
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead. def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
In [28]:
plt.imshow(ws)
plt.imshow(mark_boundaries(im,ws))
Out[28]:
<matplotlib.image.AxesImage at 0x7fcd20fbf890>
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# segment the cell
im = imread('../data/brain.jpg')[:,:,0]
plt.figure(figsize=(10,10))
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
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plt.hist(im.flatten(),255);
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from skimage.filters import threshold_otsu
t_otsu = threshold_otsu(im)
t_otsu
Out[31]:
36
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th = im > t_otsu
plt.figure(figsize=(10,10))
plt.imshow(th)
Out[32]:
<matplotlib.image.AxesImage at 0x7fcd20415dd0>
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lab = label(th,connectivity=1)
plt.imshow(lab)
Out[33]:
<matplotlib.image.AxesImage at 0x7fcd20186b50>
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from skimage.measure import regionprops
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props = regionprops(lab)
brain = (lab==7).astype(np.uint8)
pp = skr.maximum(brain,disk(3))
pp = skr.minimum(pp,disk(3))
plt.imshow(pp)
Out[35]:
<matplotlib.image.AxesImage at 0x7fcd2011b2d0>
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for p in props:
print(p.area, p.label)
1459 1 5 2 1 3 3 4 1 5 16 6 6323 7 1 8 2 9 1 10 1 11 1 12 1 13 16 14 1 15 1 16 1 17 2 18 2 19 2 20 2 21 30 22 1 23 1 24 1 25 1 26 2 27 2 28 5 29 1 30 1 31 2 32 1 33 13 34
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